Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: receiving information at an online system describing interactions by a user of the online system with an application executing on a client device; maintaining the information describing interactions by the user of the online system with the application at the online system; obtaining a future time from presentation of a content item associated with the application for which one or more metrics describing engagement with the application by the user is to be determined, the future time comprising a length of time from presentation of the content item associated with the application to engagement by the user with the application; retrieving interactions by the user of the online system with the application occurring within a time period that is greater than a threshold amount of time prior to the future time; generating one or more models based on the retrieved interactions, each model determining one or more metrics describing engagement with the application at the future time; storing the generated one or more models in association with the user and with the application; presenting content associated with the application to the user via the online system for a duration; applying the one or more models to interactions with the application occurring during different intervals of the duration to determine the one or more metrics describing engagement with the application for the different intervals of the duration; and selecting additional content associated with the application based on the one or more metrics describing engagement with the application for the different intervals of the duration.
This invention relates to predicting user engagement with applications based on historical interaction data to optimize content presentation. The method involves tracking user interactions with an application on a client device through an online system, storing this interaction data, and using it to forecast future engagement metrics. A future time is determined for measuring engagement, defined as the time between presenting content related to the application and the user's subsequent interaction with it. The system retrieves past interactions occurring before this future time, within a threshold period, to train predictive models. These models estimate engagement metrics at the future time based on historical behavior. The trained models are stored and linked to the user and application. When content is presented to the user, the models analyze engagement during different intervals of the presentation duration. The system then selects additional content based on these engagement metrics, improving content relevance and user experience. The approach enables dynamic content optimization by leveraging predictive analytics on historical interaction patterns.
2. The method of claim 1 , wherein a metric describing engagement with the application at the future time comprises an amount of time the user will spend using the application at and after the future time.
A system and method predict user engagement with a software application by analyzing historical usage data to forecast future interaction patterns. The invention addresses the challenge of anticipating user behavior to optimize resource allocation, content delivery, or system performance. By processing past usage metrics, such as session duration, frequency, and activity levels, the system generates a predictive model that estimates engagement at a specified future time. This includes calculating the expected duration a user will spend interacting with the application after that future time. The model may incorporate additional factors like user demographics, device characteristics, or contextual data to refine accuracy. The predicted engagement metric enables proactive adjustments, such as preloading content, scaling server resources, or personalizing recommendations to enhance user experience and system efficiency. The method ensures dynamic adaptation to evolving user behavior, improving both application performance and user satisfaction.
3. The method of claim 1 , wherein a metric describing engagement with the application at the future time comprises a number of one or more particular actions that the user will perform via the application at and after the future time.
This invention relates to predicting user engagement with a software application by analyzing historical user behavior to forecast future interactions. The method addresses the challenge of accurately estimating how users will engage with an application over time, which is valuable for optimizing user experience, resource allocation, and marketing strategies. The method involves collecting historical data on user actions within the application, such as clicks, logins, or content consumption. This data is processed to identify patterns and trends, which are then used to generate a predictive model. The model estimates future user engagement by calculating a metric that quantifies the expected number of specific actions a user will perform at and after a future time. These actions may include interactions like logging in, navigating menus, or completing tasks. The prediction is based on the user's past behavior and may incorporate additional factors such as time of day, device type, or application updates. By forecasting engagement, the system enables proactive adjustments, such as personalized recommendations, performance optimizations, or targeted notifications, to enhance user satisfaction and retention. The method improves upon traditional approaches by focusing on actionable, quantifiable metrics rather than broad engagement estimates.
4. The method of claim 1 , wherein a metric describing engagement with the application at the future time comprises an amount of compensation the application will receive from the user during an interval starting at the future time.
This invention relates to systems for predicting user engagement with an application and optimizing application behavior based on future engagement metrics. The problem addressed is the need to anticipate how users will interact with an application at a future time to improve performance, such as by adjusting content delivery or resource allocation. The method involves analyzing user data to predict engagement with an application at a specified future time. Engagement is quantified using metrics that include the amount of compensation the application expects to receive from the user during a defined interval starting at that future time. This compensation may represent financial transactions, in-app purchases, or other forms of value exchange. The system uses historical user behavior, contextual data, and predictive models to estimate these future engagement metrics. Additionally, the method may involve adjusting the application's behavior based on the predicted engagement. For example, the system could prioritize content delivery, allocate computational resources, or modify user interface elements to maximize engagement and compensation. The approach ensures that the application operates efficiently by aligning its operations with anticipated user interactions. The invention is particularly useful for applications where user engagement directly impacts revenue, such as subscription-based services, e-commerce platforms, or advertising-driven apps. By forecasting engagement and compensation, the system enables proactive optimization, improving user experience and financial outcomes.
5. The method of claim 1 , wherein the received information describing interactions by the user of the online system with the application executing on the client device is selected from a group consisting of: an amount of time spent by the user using the application, a frequency with which the user accesses the application, a number of times the user accesses the application, and any combination thereof.
This invention relates to tracking and analyzing user interactions with an application on a client device within an online system. The problem addressed is the need to gather detailed usage data to assess user engagement with the application. The method involves collecting information about how a user interacts with the application, including metrics such as the amount of time spent using the application, the frequency of access, and the number of times the application is accessed. These metrics can be used individually or in combination to evaluate user behavior. The collected data helps the online system understand user engagement patterns, which can be used for various purposes, such as personalizing content, improving user experience, or optimizing application performance. The method ensures that the interaction data is accurately captured and analyzed to provide meaningful insights into user activity. This approach enhances the ability to monitor and respond to user behavior dynamically, leading to more effective application management and user retention strategies.
6. The method of claim 1 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises a number of one or more particular actions performed via the application by the user.
This invention relates to tracking and analyzing user interactions with an online system through an application on a client device. The problem addressed is the need to accurately capture and process user behavior data to improve system functionality, personalization, or other services. The method involves receiving information about user interactions with an application executing on a client device. This information includes a count of one or more specific actions performed by the user via the application. The actions may include clicks, swipes, selections, or other user inputs. The system processes this interaction data to derive insights, such as user preferences, engagement levels, or behavior patterns. These insights can be used to enhance the user experience, optimize application performance, or tailor content recommendations. The method may also involve comparing the interaction data against predefined criteria or historical data to identify trends or anomalies. For example, frequent clicks on a particular feature may indicate high user interest, while a sudden drop in interactions may signal a usability issue. The system may then adjust its operations based on these findings, such as modifying the application interface, prioritizing certain features, or triggering notifications. The invention aims to provide a more responsive and adaptive online system by leveraging detailed user interaction data to drive improvements in functionality and user engagement.
7. The method of claim 1 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises an amount of compensation received by the application from the user.
This invention relates to tracking user interactions with applications on client devices, particularly in online systems where applications may receive compensation from users. The problem addressed is the need to accurately monitor and analyze user engagement, including financial transactions, to improve system performance, user experience, or monetization strategies. The method involves receiving information about user interactions with an application running on a client device, where the interactions include the amount of compensation paid by the user to the application. This compensation data is used to assess user behavior, engagement levels, or the application's revenue generation. The system may also track other interaction metrics, such as time spent, frequency of use, or specific actions taken within the application. By analyzing this data, the online system can optimize application features, adjust pricing models, or target users more effectively. The method may involve processing the compensation data in real-time or periodically to derive insights, which can then be used to enhance the application's functionality or the user's experience. The system may also compare compensation data across different users or applications to identify trends or anomalies. This approach ensures that the online system can dynamically adapt to user preferences and market conditions.
8. The method of claim 1 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises a number of one or more actions performed by the user after being presented with certain content via the application.
This invention relates to tracking user interactions with online applications to improve content delivery. The problem addressed is the need for systems to accurately measure and analyze user engagement with digital content to enhance personalization and relevance. The method involves receiving interaction data from a client device where an application is running. This data includes details about user actions taken after being presented with specific content. The actions may include clicks, swipes, views, or other engagement metrics. The system processes this data to determine how users respond to different types of content, enabling better content recommendations or targeted advertising. The method may also involve comparing interaction patterns across multiple users to identify trends or preferences. By analyzing these interactions, the system can refine content delivery strategies, ensuring users are shown more relevant or engaging material. The approach helps optimize user experience by dynamically adjusting content based on real-time or historical interaction data. This technique is particularly useful in social media, e-commerce, or digital advertising platforms where understanding user behavior is critical for improving engagement and retention. The system may also integrate with other data sources, such as user profiles or contextual information, to further enhance content personalization. The goal is to create a more responsive and adaptive content delivery mechanism that aligns with user interests and behaviors.
9. The method of claim 1 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises a number of connections between the user and other users of the application via the application.
This invention relates to analyzing user interactions within an online system, particularly focusing on social connections formed through an application on a client device. The problem addressed is the need to assess user engagement and behavior based on their interactions within the application, including social connections with other users. The method involves receiving information about a user's interactions with the application, specifically tracking the number of connections the user establishes with other users via the application. These connections represent relationships or interactions that occur within the application's environment. The system processes this data to evaluate the user's activity level, social engagement, or other relevant metrics. The method may also involve comparing this data against predefined criteria or thresholds to determine user behavior patterns, engagement levels, or other analytical insights. The invention may further include additional steps such as generating recommendations, adjusting application features, or personalizing content based on the analyzed interaction data. The goal is to enhance user experience, improve engagement, or optimize application functionality by leveraging the social connection data collected from user interactions. This approach helps the online system better understand user behavior and tailor its services accordingly.
10. The method of claim 1 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises an amount of interactions by other users with content provided to the application by the user.
This invention relates to online systems that analyze user interactions with applications to improve content delivery. The problem addressed is the need for online systems to better understand user engagement and content performance by tracking not only direct interactions by a user but also how other users engage with content shared by that user. The invention provides a method for an online system to receive interaction data from a client device running an application, where the data includes both the user's own interactions with the application and the interactions of other users with content provided by the user. This allows the system to assess the broader impact of a user's shared content, enabling more accurate recommendations, personalized content delivery, and improved user experience. The method involves processing this combined interaction data to determine engagement metrics, which can then be used to refine content suggestions, prioritize content in feeds, or adjust algorithmic recommendations. By incorporating both direct and indirect interaction data, the system gains a more comprehensive view of user influence and content relevance, addressing limitations in traditional engagement tracking that only consider direct user actions. This approach enhances the ability of online systems to deliver content that resonates with users and their networks.
11. A method comprising: receiving information at an online system describing interactions by a user of the online system with an application executing on a client device; maintaining the information describing interactions by the user of the online system with the application at the online system; obtaining a future time from presentation of a content item associated with the application for which one or more metrics describing engagement with the application by the user is to be determined, the future time comprising a length of time from presentation of the content item associated with the application to engagement by the user with the application; retrieving interactions by the user of the online system with the application occurring within a time period that is greater than a threshold amount of time prior to the future time; generating one or more models based on the retrieved interactions, each model determining one or more metrics describing engagement with the application at the future time; and storing the generated one or more models in association with the user and with the application.
This invention relates to predicting user engagement with an application based on historical interaction data. The problem addressed is determining future user engagement metrics for an application after a content item associated with the application is presented. The method involves receiving and storing interaction data from a user's engagement with an application on an online system. A future time is identified, representing the duration from content presentation to expected user engagement. The system retrieves past interactions occurring within a time window exceeding a predefined threshold before this future time. Using these interactions, one or more predictive models are generated to estimate engagement metrics at the future time. These models are then stored in association with the user and the application. The approach enables dynamic prediction of user behavior based on historical patterns, improving content targeting and application engagement strategies. The models account for temporal relevance by focusing on interactions within a specific timeframe relative to the future engagement event. This method enhances the ability to forecast user responses to content, optimizing application promotion and user experience.
12. The method of claim 11 , wherein a metric describing engagement with the application at the future time is selected from a group consisting of: an amount of time the user will spend using the application at and after the future time, a number of one or more particular actions that the user will perform via the application at and after the future time, an amount of compensation the application will receive from the user during an interval starting at the future time and any combination thereof.
This invention relates to predicting user engagement with an application to optimize future interactions. The method involves forecasting how a user will engage with an application at a specified future time, using metrics such as the duration of usage, the frequency of specific actions performed, or the amount of compensation the application may receive from the user. These predictions are based on historical user behavior data, which is analyzed to determine patterns and trends. The system then selects one or more engagement metrics to evaluate, such as the total time the user will spend on the application, the number of specific actions they will perform, or the financial compensation the application will earn from the user during a defined period starting at the future time. The method may also combine these metrics to provide a comprehensive assessment of future engagement. This approach enables applications to tailor content, advertisements, or incentives to maximize user retention and monetization. The invention is particularly useful for digital platforms, social media, or subscription-based services where understanding future user behavior is critical for strategic decision-making.
13. The method of claim 11 , wherein the received information describing interactions by the user of the online system with the application executing on the client device is selected from a group consisting of: an amount of time spent by the user using the application, a frequency with which the user accesses the application, a number of times the user accesses the application, and any combination thereof.
This invention relates to tracking and analyzing user interactions with an application on a client device within an online system. The problem addressed is the need for detailed user engagement metrics to improve application performance, personalization, or targeted content delivery. The method involves collecting and processing data on how users interact with the application, including specific metrics such as the duration of use, frequency of access, and the total number of access instances. These metrics are used to assess user engagement levels, which can then inform decisions related to application optimization, user experience enhancements, or content recommendations. The system may also combine multiple interaction metrics to derive more comprehensive insights into user behavior. The collected data is processed to generate actionable insights, which can be used to tailor the application's functionality or content to better meet user needs. This approach enables more precise user engagement analysis, leading to improved application usability and user satisfaction.
14. The method of claim 11 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises a number of one or more particular actions performed via the application by the user.
This invention relates to tracking and analyzing user interactions with applications on client devices within an online system. The problem addressed is the need to accurately capture and process user behavior data to improve system functionality, personalization, or other services. The method involves receiving information describing interactions by a user with an application executing on a client device. This information includes a count of one or more specific actions performed by the user via the application. The actions may include clicks, selections, inputs, or other interactions relevant to the application's functionality. The received data is then processed to extract meaningful insights, such as usage patterns, preferences, or engagement metrics. These insights can be used to enhance the user experience, optimize application performance, or tailor content recommendations. The method may also involve comparing the interaction data against predefined criteria or historical data to identify trends, anomalies, or opportunities for improvement. The system may further adjust application behavior, notifications, or content delivery based on the analyzed interactions. The approach ensures that user engagement is continuously monitored and leveraged to refine the application's operation within the online system.
15. The method of claim 11 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises an amount of compensation received by the application from the user.
This invention relates to tracking user interactions with applications on client devices within an online system, particularly focusing on financial transactions. The method involves monitoring and recording user activities, including compensation paid by the user to the application. The system collects data on these interactions, which may include payments or other forms of compensation, to analyze user behavior and engagement with the application. This data can be used to optimize application performance, improve user experience, or adjust monetization strategies. The method ensures that financial transactions between users and applications are logged and processed within the online system, providing insights into how users value or utilize the application. By tracking compensation, the system can identify trends, assess the effectiveness of monetization models, and refine application features based on user spending patterns. The approach enhances the ability to measure the economic impact of user interactions, supporting data-driven decision-making for application developers and online platforms.
16. The method of claim 11 , wherein the received information describing interactions by the user of the online system with the application executing on the client device comprises a number of one or more actions performed by the user after being presented with certain content via the application.
This invention relates to tracking and analyzing user interactions with online applications to improve content delivery. The problem addressed is the need for more accurate and detailed user engagement data to enhance personalized content recommendations. The method involves receiving information about a user's interactions with an application on a client device, such as a mobile or web app. This interaction data includes the number and type of actions the user performs after being presented with specific content. For example, the system records whether the user clicks, likes, shares, or spends time viewing certain content. The data is then used to assess the user's engagement level and preferences, which helps tailor future content delivery. The system may also track additional contextual factors, such as the timing, duration, and sequence of actions, to refine engagement analysis. By analyzing this data, the system can identify patterns in user behavior, such as which types of content lead to higher engagement. This information is then used to optimize content recommendations, ensuring users receive more relevant and engaging content. The method improves upon existing systems by providing a more granular and actionable understanding of user behavior, leading to better content personalization and user experience.
17. The method of claim 11 , wherein the received information describing interactions by the user of the online system with the application executing on the client device is selected from a group consisting of: a number of connections between the user and other users of the application via the application, an amount of interactions by other users with content provided to the application by the user, and any combination thereof.
This invention relates to tracking and analyzing user interactions within an online system, particularly focusing on how users engage with applications on client devices. The problem addressed is the need for a more comprehensive understanding of user behavior to improve application performance, user experience, or system recommendations. The method involves collecting and processing data about a user's interactions with an application running on a client device. Specifically, the collected data includes metrics such as the number of connections the user establishes with other users through the application and the frequency or volume of interactions other users have with content shared by the user. These interactions may include actions like viewing, liking, commenting, or sharing the user's content. The system may also analyze combinations of these metrics to derive insights about user engagement patterns. By tracking these interactions, the online system can assess the user's activity level, social influence, or content popularity, which can be used to refine recommendations, personalize content, or optimize application features. The method ensures that the collected data is relevant to measuring user engagement and social dynamics within the application.
18. A computer program product comprising a non-transitory computer readable medium having instructions encoded thereon that, when executed by a processor, cause the processor to: receive information at an online system describing interactions by a user of the online system with an application executing on a client device; maintain the information describing interactions by the user of the online system with the application at the online system; obtain a future time from presentation of a content item associated with the application for which one or more metrics describing engagement with the application by the user is to be determined, the future time comprising a length of time from presentation of the content item associated with the application to engagement by the user with the application; retrieve interactions by the user of the online system with the application occurring within a time period that is greater than a threshold amount of time prior to the future time; generate one or more models based on the retrieved interactions, each model determining one or more metrics describing engagement with the application at the future time; and store the generated one or more models in association with the user and with the application.
This invention relates to predicting user engagement with applications based on historical interaction data. The system receives and stores information about a user's interactions with an application on an online platform. When a content item associated with the application is presented, the system determines a future time for measuring engagement metrics, such as the time between content presentation and user interaction. The system then retrieves the user's past interactions with the application, focusing on those occurring within a specified time window before the future time. Using this historical data, the system generates predictive models that estimate future engagement metrics, such as click-through rates or retention rates. These models are stored and linked to the user and the application, enabling personalized engagement predictions. The approach improves targeting and content delivery by leveraging temporal patterns in user behavior. The system dynamically adjusts predictions based on recent interactions, enhancing accuracy over time. This method is particularly useful for online advertising, app recommendations, and user retention strategies.
19. The computer program product of claim 18 , wherein a metric describing engagement with the application at the future time is selected from a group consisting of: an amount of time the user will spend using the application at and after the future time, a number of one or more particular actions that the user will perform via the application at and after the future time, an amount of compensation the application will receive from the user during an interval starting at the future time and any combination thereof.
This invention relates to a computer program product for predicting user engagement with an application. The technology addresses the problem of estimating future user behavior to optimize application performance, monetization, or user experience. The system analyzes historical user data to forecast engagement metrics at a specified future time. These metrics include the amount of time a user will spend using the application, the number of specific actions the user will perform, and the compensation the application will receive from the user during a defined interval starting at the future time. The system may also combine these metrics to provide a comprehensive engagement prediction. By leveraging predictive analytics, the application can dynamically adjust features, content, or monetization strategies to enhance user retention and revenue. The invention improves upon traditional engagement tracking by focusing on forward-looking metrics rather than historical data alone, enabling proactive decision-making. The system is designed to integrate with existing applications, allowing developers to implement predictive engagement models without significant architectural changes. This approach benefits both users and application providers by aligning content and incentives with anticipated behavior, leading to a more personalized and efficient application experience.
20. The computer program product of claim 18 , wherein the non-transitory computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: generate the one or more metrics describing engagement with the application at the future time by applying the generated one or more models to information associated with the user by the online system; and select content associated with the application for presentation to the user based on the generated one or more metrics describing engagement with the application at the future time.
This invention relates to predictive analytics for user engagement in online systems, particularly for applications within such systems. The problem addressed is the challenge of dynamically selecting and presenting content to users based on their predicted future engagement with an application, rather than relying solely on past behavior or static metrics. The system generates predictive models that forecast user engagement with an application at a future time. These models are trained using historical data associated with the user and the application. The system then applies these models to current or recent user data to generate metrics that quantify the likelihood of future engagement. Based on these predictive metrics, the system selects and presents content to the user that is optimized for their anticipated future interaction with the application. This approach improves content relevance and user experience by proactively aligning content delivery with predicted user behavior. The invention also includes mechanisms to refine the predictive models over time by incorporating feedback from user interactions, ensuring the models remain accurate and adapt to changing user preferences. The system may further adjust the selection of content based on additional contextual factors, such as time of day or device type, to enhance personalization. This method enables online systems to dynamically tailor content presentation to individual users, increasing engagement and satisfaction.
Unknown
August 25, 2020
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